Pogled na podatke nakon čišćenja i transformacija¶
In [ ]:
import numpy as np
import pandas as pd
from geopy.distance import geodesic
import numpy as np
import matplotlib.pyplot as plt
from shapely import Point
import geopandas as gpd
import warnings
warnings.filterwarnings('ignore')
In [ ]:
df = pd.read_csv('dataset/transformed_deliverytime.csv')
df.head()
Out[ ]:
| Unnamed: 0 | ID | Delivery_person_ID | Delivery_person_Age | Delivery_person_Ratings | Restaurant_latitude | Restaurant_longitude | Delivery_location_latitude | Delivery_location_longitude | Weatherconditions | ... | Vehicle_condition | Type_of_order | Type_of_vehicle | multiple_deliveries | Festival | City | Time_taken(min) | Datetime_Ordered | Datetime_Picked | distance(km) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0x4607 | INDORES13DEL02 | 37 | 4.9 | 22.745049 | 75.892471 | 22.765049 | 75.912471 | Sunny | ... | 2 | snack | motorcycle | 0 | 0 | Urban | 24 | 2022-03-19 11:30:00 | 2022-03-19 11:45:00 | 3.020737 |
| 1 | 1 | 0xb379 | BANGRES18DEL02 | 34 | 4.5 | 12.913041 | 77.683237 | 13.043041 | 77.813237 | Stormy | ... | 2 | snack | scooter | 1 | 0 | Metropolitian | 33 | 2022-03-25 19:45:00 | 2022-03-25 19:50:00 | 20.143737 |
| 2 | 2 | 0x5d6d | BANGRES19DEL01 | 23 | 4.4 | 12.914264 | 77.678400 | 12.924264 | 77.688400 | Sandstorms | ... | 0 | drinks | motorcycle | 1 | 0 | Urban | 26 | 2022-03-19 08:30:00 | 2022-03-19 08:45:00 | 1.549693 |
| 3 | 3 | 0x7a6a | COIMBRES13DEL02 | 38 | 4.7 | 11.003669 | 76.976494 | 11.053669 | 77.026494 | Sunny | ... | 0 | buffet | motorcycle | 1 | 0 | Metropolitian | 21 | 2022-04-05 18:00:00 | 2022-04-05 18:10:00 | 7.774497 |
| 4 | 4 | 0x70a2 | CHENRES12DEL01 | 32 | 4.6 | 12.972793 | 80.249982 | 13.012793 | 80.289982 | Cloudy | ... | 1 | snack | scooter | 1 | 0 | Metropolitian | 30 | 2022-03-26 13:30:00 | 2022-03-26 13:45:00 | 6.197898 |
5 rows × 21 columns
In [ ]:
def df_to_gdf(df, lat_col_name, long_col_name):
gdf = gpd.GeoDataFrame(
geometry=gpd.points_from_xy(df[long_col_name], df[lat_col_name]),
crs="EPSG:4326"
)
return gdf
Pregled lokacija restorana¶
In [ ]:
restaurant_gdf = df_to_gdf(df, 'Restaurant_latitude', 'Restaurant_longitude')
restaurant_gdf.explore()
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook